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Discrimination performance in illness-death models with interval-censored disease data.

Marta Spreafico1,2, Anja J Rueten-Budde1, Hein Putter1,2

  • 1Mathematical Institute, Leiden University, Leiden, the Netherlands.

Statistical Methods in Medical Research
|January 29, 2026
PubMed
Summary
This summary is machine-generated.

Ignoring interval-censored disease data in illness-death models can impact discrimination performance. Accounting for interval-censoring is crucial for accurate model parameter estimation and performance evaluation in clinical studies.

Keywords:
AUCdiscriminationillness-death modelinterval-censoringtime-dependent disease marker

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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Survival Analysis

Background:

  • Illness-death models are standard for disease progression analysis.
  • Clinical data often features interval-censored disease onset due to scheduled visits.
  • Ignoring interval-censoring can bias model performance evaluation.

Purpose of the Study:

  • To assess the impact of ignoring interval-censored disease data on illness-death model discrimination.
  • To compare different estimation methods for interval-censored illness-death data.
  • To evaluate discrimination using time-specific area under the ROC curve.

Main Methods:

  • Simulation study using Weibull transition hazards with interval-censored data.
  • Comparison of Cox model (ignoring interval-censoring) vs. interval-censored illness-death models (msm, SmoothHazard packages).
  • Application to a high-grade soft tissue sarcoma patient dataset (n=2232).

Main Results:

  • Ignoring interval-censoring significantly affects discrimination performance metrics.
  • Interval-censored models (piecewise-constant, Weibull, M-spline) provide more accurate estimates.
  • The choice of method impacts the evaluation of model discrimination.

Conclusions:

  • Accounting for interval-censoring is essential for accurate illness-death model parameter estimation.
  • Proper handling of interval-censored data is critical for reliable discrimination performance assessment.
  • This study highlights the importance of robust statistical methods in clinical research involving time-to-event data.